On-line determination of transient stability status using MLPNN

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Samples of frequency deviations of all generator buses are obtained using a sampling frequency of 32 samples per cycle. For each generator bus, the maximum frequency deviation within the first two samples is obtained. A vector is then constructed from the obtained maximum frequency deviations. The Euclidean norm of the vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using the New England test system. The prediction accuracy was found to be 99.49%.